Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Wu, Anqing; Fan, Xiangsuo; Chen, Huajin; Min, Lei; Xu, Zhihai

doi:10.1109/access.2022.3164184

Cited by 3 publications

(4 citation statements)

References 30 publications

(23 reference statements)

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“…We will compare our algorithm with nine advanced algorithms in the weak object detection field on six sequences representing image detection effects [ 31 ]. We will also evaluate our algorithm using performance metrics such as SSIM, BSF, SNR, and ROC curves [ 32 ]. Figure 4 shows the six sequential images used in the experiments.…”

Section: Experiments and Analysismentioning

confidence: 99%

Dim and Small Target Detection with a Combined New Norm and Self-Attention Mechanism of Low-Rank Sparse Inversion

Min,

Wu,

Fan

et al. 2023

Sensors

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Methods for detecting small infrared targets in complex scenes are widely utilized across various domains. Traditional methods have drawbacks such as a poor clutter suppression ability and a high number of edge residuals in the detection results in complex scenes. To address these issues, we propose a method based on a joint new norm and self-attention mechanism of low-rank sparse inversion. Firstly, we propose a new tensor nuclear norm based on linear transformation, which globally constrains the low-rank characteristics of the image background and makes full use of the structural information among tensor slices to better approximate the rank of the non-convex tensor, thus achieving effective background suppression. Secondly, we construct a self-attention mechanism in order to constrain the sparse characteristics of the target, which further eliminates any edge residuals in the detection results by transforming the local feature information into a weight matrix to further constrain the target component. Finally, we use the alternating direction multiplier method to decompose the newly reconstructed objective function and introduce a reweighted strategy to accelerate the convergence speed of the model. The average values of the three evaluation metrics, SSIM, BSF, and SNR, for the algorithm proposed in this paper are 0.9997, 467.23, and 11.72, respectively. Meanwhile, the proposed detection method obtains a higher detection rate compared with other algorithms under the same false alarm rate.

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Section: Experiments and Analysismentioning

confidence: 99%

Dim and Small Target Detection with a Combined New Norm and Self-Attention Mechanism of Low-Rank Sparse Inversion

Min,

Wu,

Fan

et al. 2023

Sensors

Self Cite

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show abstract

“…The literature has applied machine learning methods to the target detection problem by transforming it into a convex function optimization problem through compressed sensing, matrix reconstruction, and other methods. Then, the convex function is optimized to obtain the target image [9][10][11][12]. Gao [9] proposed an IPI model, which makes full use of the nonlocal autocorrelation of an image.…”

Section: Introductionmentioning

confidence: 99%

“…Wang [11] established a total variation (TV) regularization term based on the IPI model to constrain the low-rank background for a better detection effect despite a complex edge contour background. Wu [12] proposed a gradient difference regularization factor to further suppress the edge contour in the background, obtaining a better detection effect. Zhou [13] proposed a detection method combining spatial feature map regularization and l 1,2 norm based on the IPI model; the advantage of this method is to fuse data and features manifold with the help of the graph Laplacian form, deeply explore the geometric information of data and feature space, which is used to achieve further constraint on sparse components, and obtain a better target extraction effect.…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Detection Based on Joint Spatio-Temporal Filtering and L1 Norm Regularization

Chen

et al. 2022

Sensors

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Infrared target detection is often disrupted by a complex background, resulting in a high false alarm and low target recognition. This paper proposes a robust principal component decomposition model with joint spatial and temporal filtering and L1 norm regularization to effectively suppress the complex backgrounds. The model establishes a new anisotropic Gaussian kernel diffusion function, which exploits the difference between the target and the background in the spatial domain to suppress the edge contours. Furthermore, in order to suppress the dynamically changing background, we construct an inversion model that combines temporal domain information and L1 norm regularization to globally constrain the low rank characteristics of the background, and characterize the target sparse component with L1 norm. Finally, the overlapping multiplier method is used for decomposition and reconstruction to complete the target detection.Through relevant experiments, the proposed background modeling method in this paper has a better background suppression effect in different scenes. The average values of the three evaluation indexes, SSIM, BSF and IC, are 0.986, 88.357 and 18.967, respectively. Meanwhile, the proposed detection method obtains a higher detection rate compared with other algorithms under the same false alarm rate.

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“…Based on the IPI model, many scholars have improved the model to achieve better results [11][12][13][14][15]. Wang [12] proposed a full-variance principal component tracking model based on the IPI model by using full variance to constrain the model, which achieved better detection results in scenes with few edge contours.…”

Section: Introductionmentioning

confidence: 99%

Infrared Dim and Small Target Detection Based on the Improved Tensor Nuclear Norm

Fan

Chen

et al. 2022

Applied Sciences

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In the face of complex scenes with strong edge contours and high levels of noise, suppressing edge contours and noise levels is challenging with infrared dim and small target detection algorithms. Many advanced algorithms suffer from high false alarm rates when facing this problem. To solve this, a new anisotropic background feature weight function based on the infrared patch tensor (IPT) model was developed in this study to characterize the background airspace difference features by effectively combining the local features with the global features to suppress the strong edge contours in the structural tensor. Secondly, to enhance the target energy in the a priori model, an improved high-order cumulative model was proposed to establish the local significance region of the target as a way to achieve energy enhancement of the significant target in the structural tensor. Finally, the energy-enhanced structural tensor was introduced into the partial sum of the sensor nuclear norm (PSTNN) model as a local feature information weight matrix; the detection results were obtained by solving the model with the help of ADMM. A series of experiments show that the algorithm in this paper achieves better detection results compared with other algorithms.

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Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Cited by 3 publications

References 30 publications

Dim and Small Target Detection with a Combined New Norm and Self-Attention Mechanism of Low-Rank Sparse Inversion

Dim and Small Target Detection with a Combined New Norm and Self-Attention Mechanism of Low-Rank Sparse Inversion

Infrared Target Detection Based on Joint Spatio-Temporal Filtering and L1 Norm Regularization

Infrared Dim and Small Target Detection Based on the Improved Tensor Nuclear Norm

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